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Found 15 papers, 1 papers with code
Utilizing Adversarial Examples for Bias Mitigation and Accuracy Enhancement
To address this issue, we propose using adversarial images, that is images that deceive a deep neural network but not humans, as counterfactuals for fair model training.
TIBET: Identifying and Evaluating Biases in Text-to-Image Generative Models
Text-to-Image (TTI) generative models have shown great progress in the past few years in terms of their ability to generate complex and high-quality imagery.
Sparse Fusion for Multimodal Transformers
Multimodal classification is a core task in human-centric machine learning.
One-Class Meta-Learning: Towards Generalizable Few-Shot Open-Set Classification
Real-world classification tasks are frequently required to work in an open-set setting.
BLT: Balancing Long-Tailed Datasets with Adversarially-Perturbed Images
Unfortunately, this imbalance enables a visual recognition system to perform well on head classes but poorly on tail classes.
Ranked #53 on
Long-tail Learning
on ImageNet-LT
What Should I Ask? Using Conversationally Informative Rewards for Goal-Oriented Visual Dialog
In this work, we focus on the task of goal-oriented visual dialogue, aiming to automatically generate a series of questions about an image with a single objective.
What Should I Ask? Using Conversationally Informative Rewards for Goal-oriented Visual Dialog.
In this work, we focus on the task of goal-oriented visual dialogue, aiming to automatically generate a series of questions about an image with a single objective.
CLEAR: Cumulative LEARning for One-Shot One-Class Image Recognition
This work addresses the novel problem of one-shot one-class classification.
ANSAC: Adaptive Non-minimal Sample and Consensus
While RANSAC-based methods are robust to incorrect image correspondences (outliers), their hypothesis generators are not robust to correct image correspondences (inliers) with positional error (noise).
Data-Intensive Supercomputing in the Cloud: Global Analytics for Satellite Imagery
We present our experiences using cloud computing to support data-intensive analytics on satellite imagery for commercial applications.
One-Class Slab Support Vector Machine
This work introduces the one-class slab SVM (OCSSVM), a one-class classifier that aims at improving the performance of the one-class SVM.
Large Scale SfM with the Distributed Camera Model
We introduce the distributed camera model, a novel model for Structure-from-Motion (SfM).
Optimizing the Viewing Graph for Structure-From-Motion
The viewing graph represents a set of views that are related by pairwise relative geometries.
Computing Similarity Transformations From Only Image Correspondences
We propose a novel solution for computing the relative pose between two generalized cameras that includes reconciling the internal scale of the generalized cameras.
SWIGS: A Swift Guided Sampling Method
We present SWIGS, a Swift and efficient Guided Sampling method for robust model estimation from image feature correspondences.
